Speed-up ONNX models with TensorRT

ONNX models ONNX is a great initiative to standardize the structure and storage of deep neural networks. Almost all frameworks export to ONNX in one way or another. Implementations may then vary, though it’s expected to converge eventually. ONNX models are of great interest since: They can easily be exported around with their weights They can be optimized and converted to a variety of CPUs and GPUs Both interoperability and hardware access are key to replace or enhance modern software stacks with deep neural networks.

Experimenting with Vision Transformer

Transformer architectures are coming to vision tasks There’s a new breed of computer vision models in the making. This change is mostly due to the coming of the originally NLP oriented Transformer architectures to computer vision tasks. Recent advances of 2020 in this domain include the Vision Tranformer (ViT / Google) and the Visual Transformer (Berkeley / Facebook AI) for image classification. And the DETR (Facebook) and Deformable DETR (SenseTime) architectures for object detection.